Machine Learning

Machine learning is a subset of AI in which systems learn patterns from data and improve with experience instead of following explicit programming for every case. In software products, machine learning powers recommendations, forecasting, detection, classification, and personalization across categories. Buyers should evaluate ML features by their data requirements and outcomes: models need sufficient quality data, their performance must be measurable against baselines, and their behavior must be explainable where decisions affect people. Practical questions include how models are trained and updated, whether the organization's data meets the requirements, and what fallbacks exist when predictions are wrong. ML also raises governance concerns, including bias, privacy, and auditability, which matter more in regulated settings. Trial evaluation should test model output on the buyer's own data under realistic conditions rather than vendor metrics. Machine learning is transforming software, but value depends on data quality and human oversight rather than the label alone.